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Deep sequencing of a genetically heterogeneous sample : local haplotype reconstruction and read error correction

Zagordi, Osvaldo and Geyrhofer, Lukas and Roth, Volker and Beerenwinkel, Niko. (2010) Deep sequencing of a genetically heterogeneous sample : local haplotype reconstruction and read error correction. Journal of computational biology, Vol. 17, H. 3. pp. 417-428.

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Official URL: http://edoc.unibas.ch/dok/A5265615

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Abstract

We present a computational method for analyzing deep sequencing data obtained from a genetically diverse sample. The set of reads obtained from a deep sequencing experiment represents a statistical sample of the underlying population. We develop a generative probabilistic model for assigning observed reads to unobserved haplotypes in the presence of sequencing errors. This clustering problem is solved in a Bayesian fashion using the Dirichlet process mixture to define a prior distribution on the unknown number of haplotypes in the mixture. We devise a Gibbs sampler for sampling from the joint posterior distribution of haplotype sequences, assignment of reads to haplotypes, and error rate of the sequencing process, to obtain estimates of the local haplotype structure of the population. The method is evaluated on simulated data and on experimental deep sequencing data obtained from HIV samples.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Informatik > Datenanalyse (Roth)
UniBasel Contributors:Roth, Volker
Item Type:Article, refereed
Article Subtype:Research Article
Bibsysno:Link to catalogue
Publisher:Mary Ann Liebert
ISSN:1066-5277
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:22 Mar 2012 14:27
Deposited On:22 Mar 2012 13:57

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